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Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems
This paper studies the lower bound complexity for minimax optimization problem whose objective function is the average of $n$ individual smooth convex-concave functions.
Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization
This paper considers stochastic first-order algorithms for convex-concave minimax problems of the form $\min_{\bf x}\max_{\bf y}f(\bf x, \bf y)$, where $f$ can be presented by the average of $n$ individual components which are $L$-average smooth.
Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent
We propose \textit{Meta-Regularization}, a novel approach for the adaptive choice of the learning rate in first-order gradient descent methods.
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
This construction is friendly to the analysis of PIFO algorithms.
DIPPA: An improved Method for Bilinear Saddle Point Problems
This paper studies bilinear saddle point problems $\min_{\bf{x}} \max_{\bf{y}} g(\bf{x}) + \bf{x}^{\top} \bf{A} \bf{y} - h(\bf{y})$, where the functions $g, h$ are smooth and strongly-convex.
Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs
Regularized MDPs serve as a smooth version of original MDPs.
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices
We study the streaming model for approximate matrix multiplication (AMM).
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond
Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers for representing weight parameters and activations, and are often used in real-world applications due to their saving of computation resources and reproducibility of results.
A Novel Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization
This paper studies the lower bound complexity for the optimization problem whose objective function is the average of $n$ individual smooth convex functions.
A Stochastic Proximal Point Algorithm for Saddle-Point Problems
We consider saddle point problems which objective functions are the average of $n$ strongly convex-concave individual components.
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization
This paper studies the lower bound complexity for the optimization problem whose objective function is the average of $n$ individual smooth convex functions.
Hyper-Regularization: An Adaptive Choice for the Learning Rate in Gradient Descent
Specifically, we impose a regularization term on the learning rate via a generalized distance, and cast the joint updating process of the parameter and the learning rate into a maxmin problem.
Accelerated Value Iteration via Anderson Mixing
A2VI is more efficient than the modified policy iteration, which is a classical approximate method for policy evaluation.
Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks
In this paper we explore acceleration techniques for large scale nonconvex optimization problems with special focuses on deep neural networks.
